Most online marketing campaigns concentrate on enticing viewers to click on the creative. There is usually little or no ability to optimize what happens immediately after the initial click. Today most marketers don't have an effective way to optimize post-clickthrough conversion for prospective customers acquired through banner advertising, sponsorships, promotions, or email—often, the clickthrough leads directly to the home page, or to a lower level page that isn't optimized by taking into account feedback on what attributes work most effectively.
Various site analysis and reporting systems currently exist for the limited tracking of performance of visitors on or to a site. These systems update performance information on a limited periodic basis in the form of typically printed reports. The users are also provided with an some parameters to configure the delivery and tracking. In the current state-of-the-art, these reports are provided in printed form or in the electronic equivalent of printed form (for example, web-pages, spreadsheets, charts, or the like), and are manually analyzed by trained analysis personnel to derive new, improved configurations. Typically, these configurations consist of allocations of advertisements—the fraction of available visitors that are allocated to each advertisement. In particular, some advertisements may be turned off (allocated no visitors) when the analysis personnel determine them to be underperforming. This manual process is tedious and error-prone and has an inherent delay between the period of data collection and the time new advertisements are to be placed because of the large amount of data to be analyzed and the large number of parameters to be modified. Even if errors are not made and the user is able to overcome the tedium of the process, it is unlikely to yield optimal or even near-optimal recommendations for advertisement configurations. This is especially true in light of the typical delay—between a day and a week—between data collection, analysis, and a new campaign configuration based on the analysis. Further, the splash page performance may improve or deteriorate over time, such as for example if the performance of a splash page is non-stationary in a statistical sense. There are a number of potential reasons for a splash page to have non-stationary behavior in the underlying performance. For example, a splash page that promises overnight delivery may be quite effective shortly before Christmas, but much less effective on the day after Christmas. Even absent a particular identifiable event, the performance of splash pages may change over time.
In light of the current situation described above, there remains a need for an automated system for optimizing allocation parameters. There also remains a need for an automated system and method for rapidly and efficiently executing the optimized allocation parameters to show the right splash pages. Optimization is in essence a multi-dimensional optimization problem, that by-and-large cannot be timely solved using conventional tools, methods, or systems. Furthermore, optimizations on multiple dimensions are impractical to do manually and exacerbate the time delay between data collection and reconfiguration. It is noted that these problems exist independently of the type of splash page or other content, and that such issues and problems exist relative to advertisements for products and services, political campaigns, ballot measures and initiatives, media programming, lobbying, surveys, polling, news headlines, sports scores, as well as other directed marketing, promotions, surveys, news, information, other content generally, and the like.
There also remains a need for a system and method that can learn and optimize across the various other parameters to allow an advertiser (or other messaging entity) to display different splash pages (or other content or splash pages) based on a user-specific web-browsing profile which may include time-of-day, geographic location, demographic information, or the like, as well as other user-targeting information. This system should work particularly effectively in scenarios where the splash page performance may improve or deteriorate over time, for example, in situations when the performance of a splash page is non-stationary in a statistical sense.
Reporting and analysis tools available today treat splash pages as atomic entities. However, it is usually the case that splash pages may be broken down into elements, or attributes, such as color, offer, or the like. Since current reporting tools treat splash pages as atomic entities, no information is typically available to analyze the effects of changes to different attributes. Therefore, there remains a need for a system and method that: 1) generates a plurality of splash pages, each having different values of the different attributes, and 2) collects information on the performance of such splash pages. The system and method can use that information to report on the most effective attribute levels, as well as to optimize campaign performance by generating splash pages for current or future campaigns.
Different advertising campaigns may seek to optimize different metrics. There remains a need for a system and method that is sufficiently flexible to optimize a wide variety of performance metrics. For example, one simple but frequently used optimization objective is to maximize the number of user signups from a splash page. Another objective may be to maximize the total sales generated by all the consumers.
Visitors may arrive on a splash page through different channels such as email, banner ads and affiliate programs. There remains a further need to identify better performing channels from which the visitors are arriving as quickly as possible so that the use and cost associated with poorer performing channels may be terminated in favor of better performing channels.